A laboratory specimen operation process monitoring system

Abstract

The present invention relates to the field of information technology. The present invention discloses a monitoring system for the operation process flow of laboratory specimens, including: a specimen knowledge base construction module, a specimen receiving and verification module, a process flow data acquisition module, a machine fault detection module, a machine fault warning module, a specimen quality monitoring module, a specimen quality warning module, and a warning result output module. By using a barcode scanner to scan the unique identification code on the specimen to verify the specimen information, and using sensors to monitor in real time the operation data of the machine and the specimen quality parameter data during the specimen processing process, the machine operation power coefficient is calculated through an operation monitoring mathematical model to determine whether the machine operation is normal, and the abnormal results are warned and displayed. The electrolytic activity coefficient of the specimen is calculated through a specimen quality monitoring model to determine whether the specimen quality is normal, and the abnormal results are warned and displayed. Each link of the specimen operation process flow is monitored in real time to ensure the safety of the specimen.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and more particularly to a monitoring system and method for the operation process of laboratory specimens. Background Art

[0002] With the rapid development of information technology, especially the breakthroughs in fields such as computer network technology, database technology, and software development technology, powerful technical support has been provided for the development of process monitoring systems. These technologies enable the system to collect, process, store, and transmit a large amount of data in real time, providing strong support for monitoring and decision-making. The information-based management system can provide strong support for building a high-quality and high-standard sample library. The development of automation and intelligent control technology enables each link in the process to achieve automated operation and intelligent control. Each link such as the collection, transportation, preservation, and detection of specimens requires extremely high accuracy. Any error in any link will affect the accuracy of the detection results, and thus affect clinical diagnosis and treatment.

[0003] Currently, the informatization level of the existing sample libraries in our country is not high. Information and data are collected using paper records and then manually entered into the computer by the staff of the sample library. This mode is not only time-consuming and laborious, but also prone to errors. The early-established biobanks only have simple storage functions and do not conduct in-depth research on samples, making it difficult to make due contributions to translational medicine, which is mainly reflected in the following aspects: Data lag: Traditional process monitoring systems need to collect data, analyze data, and then generate reports, which takes a certain amount of time, resulting in data lag and being unable to reflect the real-time situation in a timely manner; Difficult data analysis: Lack of intelligent analysis functions, data cannot be effectively classified and stored and quickly retrieved, making it time-consuming and laborious for staff to find specific information, and it is difficult to quickly obtain the required data, affecting work efficiency and decision-making speed; Passive monitoring: Most traditional process monitoring systems can only play a role in video recording and lack the ability of active warning and intervention. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a monitoring system and method for the operation process of laboratory specimens to solve the problems existing in the above-mentioned background art.

[0005] The present invention provides the following technical solutions: A monitoring system for the operation process of laboratory specimens, comprising: a specimen knowledge base construction module, a specimen receiving and verification module, a process data acquisition module, a machine failure detection module, a machine failure warning module, a specimen quality monitoring module, a specimen quality warning module, and a warning result output module;

[0006] The specimen knowledge base construction module stores different specimen data into the specimen knowledge base according to the specimen type and assigns a unique identification code to each specimen;

[0007] The specimen receiving and verification module includes a specimen receiving unit and a specimen verification unit. After receiving the specimen, the specimen receiving unit scans the unique identification code on the specimen using a barcode scanner, enters the identification information into the system, and the specimen verification unit verifies the specimen information. After verification, the specimen is confirmed to be received.

[0008] The process flow data acquisition module includes a machine data acquisition unit and a specimen parameter acquisition unit. The machine data acquisition unit uses sensors to monitor the operation data of the machine in real time during the specimen processing, and the specimen parameter acquisition unit uses sensors to obtain the specimen quality parameter data and transmits the specimen quality parameter data to the machine fault detection module and the specimen quality monitoring module respectively.

[0009] The machine fault detection module, based on the operation data transmitted by the process flow data acquisition module, calculates the machine operation power coefficient through the operation monitoring mathematical model and transmits the calculated machine operation power coefficient to the machine fault warning module.

[0010] The machine fault warning module receives the machine operation power coefficient transmitted by the machine fault detection module, determines whether the machine operation is normal, and warns and displays the abnormal results.

[0011] The specimen quality monitoring module, based on the specimen quality parameters transmitted by the process flow data acquisition module, calculates the electrolytic activity coefficient of the specimen through the specimen quality monitoring model and transmits the calculated electrolytic activity coefficient to the specimen quality warning module.

[0012] The specimen quality warning module receives the electrolytic activity coefficient transmitted by the specimen quality monitoring module, determines whether the specimen quality is normal, and warns and displays the abnormal results.

[0013] The warning result output module receives the abnormal results and transmits the results to the user terminal.

[0014] Preferably, in the specimen knowledge base construction module, different specimen data are stored in the specimen knowledge base according to the specimen type, and the specimen data include the temperature control range and the specimen storage conditions.

[0015] Preferably, in the specimen receiving and verification module, the specimen receiving unit scans the unique identification code on the specimen using a barcode scanner, enters the identification information into the system, the system conducts a receiving calibration on the specimen, and outputs the corresponding specimen data in the specimen knowledge base. The specimen verification unit verifies whether the specimen is complete and whether necrosis occurs. After verification, the specimen is confirmed to be received.

[0016] Preferably, in the process flow data acquisition module, the machine data acquisition unit uses sensors to monitor in real time the operation data of the machine during specimen processing, including the pressure value under atmospheric conditions, the pre-stage pressure loss value during machine operation, the pressure value under exhaust conditions, the post-stage pressure loss value during machine operation, and the pre-stage air volume flow rate during machine operation. The specimen parameter acquisition unit uses sensors to obtain specimen quality parameter data, including the electric charge of positive ions during electrolysis, the electric charge of negative ions during electrolysis, the current density during electrolysis, the viscosity of the specimen electrolytic solvent, and the molar conductivity before dilution.

[0017] Preferably, in the machine fault detection module, based on the operation data transmitted by the process flow data acquisition module, the specific content of the machine operation power coefficient calculated through the operation monitoring mathematical model is as follows:

[0018] Step S01: Use an automated specimen processing machine to process the received specimens.

[0019] Step S02: Calculate the pre-stage pressure during machine operation. The calculation formula is:

[0020]

[0021] Where represents the pre-stage pressure during machine operation, represents the pressure value under atmospheric conditions, represents the pre-stage pressure loss value during machine operation;

[0022] Step S03: Calculate the post-stage pressure during machine operation. The calculation formula is:

[0023]

[0024] Where represents the post-stage pressure during machine operation, represents the pressure value under exhaust conditions, represents the post-stage pressure loss value during machine operation;

[0025] Step S04: Calculate the machine operation compression power based on Step S02 and Step S03. The calculation formula is:

[0026] Where Wp represents the machine operation compression power, represents the pre-stage air volume flow rate during machine operation, represents the adiabatic index;

[0027] Step S05: Calculate the machine operation power coefficient. The calculation formula is:

[0028]

[0029] wherein represents the machine operation power coefficient, represents the adiabatic efficiency of the machine operation.

[0030] Preferably, the machine fault warning module receives the machine operation power coefficient transmitted by the machine fault detection module and compares the machine operation power coefficient with a preset machine operation power threshold to determine whether the machine operation is normal. If the machine operation power coefficient is greater than the preset machine operation power threshold , it is determined that the machine operation is abnormal, and an alarm is displayed for the abnormal result, and the warning information of the abnormal result is transmitted to the warning result output module. If the machine operation power coefficient is less than or equal to the preset machine operation power threshold , it is determined that the machine operation is normal.

[0031] Preferably, in the specimen quality monitoring module, based on the specimen quality parameters transmitted by the process flow data acquisition module, the specific content of calculating the electrolytic activity coefficient of the specimen through the specimen quality monitoring model is as follows:

[0032] Step S01: Calculate the dielectric coefficient of the specimen, and the calculation formula is:

[0033]

[0034] wherein represents the dielectric coefficient of the specimen, represents the electric charge amount of positive ions during the electrolysis process, represents the electric charge amount of negative ions during the electrolysis process, represents the current density during the electrolysis process, represents the dielectric constant of the specimen, and T represents the thermodynamic temperature;

[0035] Step S02: Calculate the viscosity coefficient of the specimen, and the calculation formula is:

[0036]

[0037] wherein represents the viscosity coefficient of the specimen, represents the viscosity of the specimen electrolysis solvent;

[0038] Step S03: Calculate the electrolytic dilution molar conductivity according to Step S01 and Step S02, and the calculation formula is:

[0039] wherein represents the electrolytic dilution molar conductivity, represents the molar conductivity before dilution, represents the current value during the electrolysis process;

[0040] Step S04: Calculate the electrolytic activity coefficient of the specimen, and the calculation formula is:

[0041]

[0042] wherein represents the electrolytic activity coefficient of the specimen.

[0043] Preferably, the specimen quality warning module receives the electrolytic activity coefficient transmitted by the specimen quality monitoring module and compares the electrolytic activity coefficient with a preset electrolytic activity threshold to determine whether the specimen quality is normal. If the electrolytic activity coefficient is less than the preset electrolytic activity threshold , it is determined that the specimen quality is abnormal, and an alarm is displayed for the abnormal result, and the warning information of the abnormal result is transmitted to the warning result output module. If the electrolytic activity coefficient is greater than or equal to the preset electrolytic activity threshold , it is determined that the machine is operating normally.

[0044] Preferably, the warning result output module receives the warning information transmitted by the machine failure warning module and the specimen quality warning module, and transmits the warning result to the user terminal through information reminder.

[0045] A method for monitoring the operation process of laboratory specimens includes the following steps:

[0046] Step S1: Store different specimen data in the specimen knowledge base according to the specimen type, and assign a unique identification code to each specimen;

[0047] Step S2: Use a barcode scanner to scan the unique identification code on the specimen, check the specimen information, and confirm the receipt of the specimen after the check is correct;

[0048] Step S3: Use a sensor to continuously monitor the operation data of the machine and the specimen quality parameter data during the specimen processing;

[0049] Step S4: Based on the operation data transmitted by the process data acquisition module, calculate the machine operation power coefficient through the operation monitoring mathematical model;

[0050] Step S5: Determine whether the machine is operating normally according to the machine operation power coefficient, and display an alarm for the abnormal result;

[0051] Step S6: Based on the specimen quality parameters transmitted by the process flow data acquisition module, calculate the electrolytic activity coefficient of the specimen through the specimen quality monitoring model;

[0052] Step S7: Determine whether the specimen quality is normal based on the electrolytic activity coefficient, and give an early warning display for abnormal results;

[0053] Step S8: Receive the abnormal results and transmit the results to the user terminal.

[0054] Technical effects and advantages of the present invention:

[0055] The present invention is provided with a specimen knowledge base construction module, a specimen receiving and verification module, a process flow data acquisition module, a machine fault detection module, a machine fault early warning module, a specimen quality monitoring module, a specimen quality early warning module, and an early warning result output module. By using a barcode scanner to scan the unique identification code on the specimen to verify the specimen information, and using sensors to monitor the operation data of the machine and the specimen quality parameter data in real time during the specimen processing process, calculate the machine operation power coefficient through the operation monitoring mathematical model to determine whether the machine operation is normal, and give an early warning display for abnormal results. Calculate the electrolytic activity coefficient of the specimen through the specimen quality monitoring model to determine whether the specimen quality is normal, and give an early warning display for abnormal results. In short, a laboratory specimen operation process monitoring system and method monitor each link of the specimen operation process in real time to ensure the safety of the specimen. When potential risks or problems are found, the system automatically issues an early warning to remind relevant personnel to handle them in time. Description of the Drawings

[0056] Figure 1 It is a schematic diagram of the monitoring module process of a laboratory specimen operation process monitoring system.

[0057] Figure 2 It is a schematic diagram of the method steps of a laboratory specimen operation process monitoring method. Detailed Embodiments

[0058] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the drawings in the present invention. In addition, the forms of each structure described in the following embodiments are only examples. A laboratory specimen operation process monitoring system and method involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0059] Such as Figure 1As shown in the figure, the present invention provides a monitoring system for the operation process of laboratory specimens, including: a specimen knowledge base construction module, a specimen receiving and checking module, a process data acquisition module, a machine fault detection module, a machine fault warning module, a specimen quality monitoring module, a specimen quality warning module, and a warning result output module;

[0060] The specimen knowledge base construction module stores different specimen data into the specimen knowledge base according to the specimen type, and assigns a unique identification code to each specimen;

[0061] The specimen receiving and checking module includes a specimen receiving unit and a specimen checking unit. After the specimen receiving unit receives the specimen, it scans the unique identification code on the specimen using a barcode scanner and enters the identification information into the system. The specimen checking unit checks the specimen information and confirms the receipt of the specimen after the check is correct;

[0062] The process data acquisition module includes a machine data acquisition unit and a specimen parameter acquisition unit. The machine data acquisition unit uses sensors to monitor the operation data of the machine in real time during the specimen processing. The specimen parameter acquisition unit uses sensors to obtain the specimen quality parameter data and transmits the specimen quality parameter data to the machine fault detection module and the specimen quality monitoring module respectively;

[0063] The machine fault detection module calculates the machine operation power coefficient through an operation monitoring mathematical model based on the operation data transmitted by the process data acquisition module, and transmits the calculated machine operation power coefficient to the machine fault warning module;

[0064] The machine fault warning module receives the machine operation power coefficient transmitted by the machine fault detection module, judges whether the machine operation is normal, and warns and displays the abnormal result;

[0065] The specimen quality monitoring module calculates the electrolytic activity coefficient of the specimen through a specimen quality monitoring model based on the specimen quality parameters transmitted by the process data acquisition module, and transmits the calculated electrolytic activity coefficient to the specimen quality warning module;

[0066] The specimen quality warning module receives the electrolytic activity coefficient transmitted by the specimen quality monitoring module, judges whether the specimen quality is normal, and warns and displays the abnormal result;

[0067] The warning result output module receives the abnormal result and transmits the result to the user terminal.

[0068] In this embodiment, it should be specifically noted that in the specimen knowledge base construction module, different specimen data are stored into the specimen knowledge base according to the specimen type, and the specimen data includes the temperature control range and the specimen storage conditions.

[0069] In this embodiment, it should be specifically noted that in the specimen receiving and checking module, the specimen receiving unit uses a barcode scanner to scan the unique identification code on the specimen, enters the identification information into the system, and the system calibrates the received specimen and outputs the corresponding specimen data in the specimen knowledge base. The specimen checking unit checks whether the specimen is complete and whether necrosis occurs, and confirms the received specimen after passing the check.

[0070] Common barcode scanners include one-dimensional barcode scanners and two-dimensional barcode scanners. According to the type of barcode on the specimen, it is crucial to select a suitable scanner. Performance requirements: The scanner needs to have high reading speed, high reliability, and the ability to read at a long distance and in a large range to meet the scanning requirements in different environments. Specificity: Considering the particularity of the medical industry, selecting a scanner with a dedicated optimized decoding algorithm for the medical industry can improve the recognition accuracy and reduce the occurrence of misreading and missing codes.

[0071] The scanner will automatically read the information in the barcode, convert it into a digital signal for processing, and after the scanning is completed, the scanner will display the read information and transmit this information to the connected computer database for storage.

[0072] In this embodiment, it should be specifically noted that in the process flow data acquisition module, the machine data acquisition unit uses sensors to monitor the operation data of the machine during the specimen processing in real time, including the pressure value under atmospheric conditions, the pre-stage pressure loss value during the machine operation, the pressure value under the exhaust state, the post-stage pressure loss value during the machine operation, and the pre-stage air volume flow rate during the machine operation. The specimen parameter acquisition unit uses sensors to obtain the specimen quality parameter data, including the charge amount of positive ions during the electrolysis process, the charge amount of negative ions during the electrolysis process, the current density during the electrolysis process, the viscosity of the specimen electrolytic solvent, and the molar conductivity before dilution.

[0073] In this embodiment, it should be specifically noted that in the machine fault detection module, based on the operation data transmitted by the process flow data acquisition module, the specific content of the machine operation power coefficient calculated through the operation monitoring mathematical model is as follows:

[0074] Step S01: Use an automated specimen processing machine to process the received specimen.

[0075] Step S02: Calculate the pre-stage pressure during the machine operation. The calculation formula is:

[0076]

[0077] Where represents the pre-stage pressure during the machine operation, represents the pressure value under atmospheric conditions, Represents the pre-stage pressure loss value during the operation of the machine;

[0078] Step S03: Calculate the post-stage pressure during the operation of the machine. The calculation formula is:

[0079]

[0080] Where Represents the post-stage pressure during the operation of the machine, Represents the pressure value under the exhaust state, Represents the post-stage pressure loss value during the operation of the machine;

[0081] Step S04: Calculate the compression power of the machine according to Step S02 and Step S03. The calculation formula is:

[0082] Where Wp represents the compression power of the machine during operation, Represents the pre-stage air volume flow rate during the operation of the machine, Represents the adiabatic index;

[0083] Step S05: Calculate the power coefficient of the machine. The calculation formula is:

[0084]

[0085] Where Represents the power coefficient of the machine during operation, Represents the adiabatic efficiency of the machine during operation.

[0086] In this embodiment, it should be specifically noted that the machine fault warning module receives the power coefficient of the machine during operation transmitted by the machine fault detection module , and compares the power coefficient of the machine during operation with the preset power threshold of the machine during operation to determine whether the machine is operating normally. If the power coefficient of the machine during operation is greater than the preset power threshold of the machine during operation , it is determined that the machine is operating abnormally, and an alarm is displayed for the abnormal result, and the warning information of the abnormal result is transmitted to the warning result output module. If the power coefficient of the machine during operation is less than or equal to the preset power threshold of the machine during operation , it is determined that the machine is operating normally.

[0087] In this embodiment, it should be specifically noted that in the specimen quality monitoring module, based on the specimen quality parameters transmitted by the process flow data acquisition module, the specific content of the electrolytic activity coefficient of the specimen calculated by the specimen quality monitoring model is as follows:

[0088] Step S01: Calculate the dielectric constant of the specimen, and the calculation formula is:

[0089]

[0090] where represents the dielectric constant of the specimen, represents the electric charge quantity of positive ions during the electrolysis process, represents the electric charge quantity of negative ions during the electrolysis process, represents the current density during the electrolysis process, represents the dielectric constant of the specimen, and T represents the thermodynamic temperature;

[0091] Step S02: Calculate the viscosity coefficient of the specimen, and the calculation formula is:

[0092]

[0093] where represents the viscosity coefficient of the specimen, represents the viscosity of the electrolytic solvent of the specimen;

[0094] Step S03: Calculate the electrolytic dilution molar conductivity according to Step S01 and Step S02, and the calculation formula is:

[0095] where represents the electrolytic dilution molar conductivity, represents the molar conductivity before dilution, represents the current value during the electrolysis process;

[0096] Step S04: Calculate the electrolytic activity coefficient of the specimen, and the calculation formula is:

[0097]

[0098] where represents the electrolytic activity coefficient of the specimen.

[0099] In this embodiment, it should be specifically noted that the specimen quality warning module receives the electrolytic activity coefficient transmitted by the specimen quality monitoring module , and compares the electrolytic activity coefficient with the preset electrolytic activity threshold to judge whether the specimen quality is normal. If the electrolytic activity coefficient is less than the preset electrolytic activity threshold , it is determined that the specimen quality is abnormal, and an alarm is displayed for the abnormal result, and the warning information of the abnormal result is transmitted to the warning result output module. If the electrolytic activity coefficient is greater than or equal to the preset electrolytic activity threshold , it is determined that the machine is operating normally.

[0100] In this embodiment, specifically, the warning result output module receives the warning information transmitted by the machine fault warning module and the specimen quality warning module, and transmits the warning result to the user terminal through information reminder.

[0101] Such as Figure 2 shown, in this embodiment, specifically, the method for using a laboratory specimen operation process monitoring system and method includes the following steps:

[0102] Step S1: Store different specimen data in the specimen knowledge base according to the specimen type, and assign a unique identification code to each specimen;

[0103] Step S2: Use a barcode scanner to scan the unique identification code on the specimen, check the specimen information, and confirm the receipt of the specimen after verification;

[0104] Step S3: Use sensors to monitor the operation data of the machine and the specimen quality parameter data in real time during the specimen processing;

[0105] Step S4: Based on the operation data transmitted by the process flow data acquisition module, calculate the machine operation power coefficient through the operation monitoring mathematical model;

[0106] Step S5: Judge whether the machine is operating normally according to the machine operation power coefficient, and display a warning for the abnormal result;

[0107] Step S6: Based on the specimen quality parameters transmitted by the process flow data acquisition module, calculate the electrolytic activity coefficient of the specimen through the specimen quality monitoring model;

[0108] Step S7: Judge whether the specimen quality is normal based on the electrolytic activity coefficient, and display a warning for the abnormal result;

[0109] Step S8: Receive the abnormal result and transmit the result to the user terminal.

[0110] In this embodiment, it should be specifically noted that the main difference between this embodiment and the prior art is that this embodiment is provided with a specimen knowledge base construction module, a specimen reception and verification module, a process flow data acquisition module, a machine fault detection module, a machine fault warning module, a specimen quality monitoring module, a specimen quality warning module, and a warning result output module. By using a barcode scanner to scan the unique identification code on the specimen to verify the specimen information, sensors are used to monitor the operation data of the machine and the specimen quality parameter data in real time during the specimen processing. The machine operation power coefficient is calculated through the operation monitoring mathematical model to determine whether the machine is operating normally, and the abnormal results are warned and displayed. The electrolytic activity coefficient of the specimen is calculated through the specimen quality monitoring model to determine whether the specimen quality is normal, and the abnormal results are warned and displayed. In short, a laboratory specimen operation process monitoring system and method monitor each link of the specimen operation process in real time to ensure the safety of the specimen. When potential risks or problems are found, the system automatically issues a warning to remind relevant personnel to handle them in a timely manner.

[0111] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0112] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or replacements, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A laboratory specimen operation process monitoring system, characterized in that: include: Specimen knowledge base construction module, specimen receiving and checking module, process data acquisition module, machine fault detection module, machine fault early warning module, specimen quality monitoring module, specimen quality early warning module and early warning result output module; The specimen knowledge base construction module stores different specimen data into the specimen knowledge base according to the specimen type, and formulates a unique identification code for each specimen; The specimen receiving and checking module comprises a specimen receiving unit and a specimen checking unit. After receiving the specimen, the specimen receiving unit uses a barcode scanner to scan the unique identification code on the specimen and enters the identification information into the system. The specimen checking unit checks the specimen information and confirms receipt of the specimen after checking that it is correct. The process data acquisition module includes a machine data acquisition unit and a specimen parameter acquisition unit. The machine data acquisition unit uses sensors to monitor the operation data of the machine in the specimen processing process in real time. The specimen parameter acquisition unit uses sensors to acquire specimen quality parameter data and transmits the specimen quality parameter data to the machine fault detection module and the specimen quality monitoring module respectively. The machine fault detection module calculates the machine operation power coefficient through the operation monitoring mathematical model based on the operation data transmitted by the process flow data acquisition module, and transmits the calculated machine operation power coefficient to the machine fault warning module; The machine fault warning module receives the machine operation power coefficient transmitted by the machine fault detection module, determines whether the machine operation is normal, and displays an early warning of abnormal results; The sample quality monitoring module calculates the electrolysis activity coefficient of the sample through the sample quality monitoring model based on the sample quality parameters transmitted by the process data acquisition module, and transmits the calculated electrolysis activity coefficient to the sample quality early warning module; The sample quality early warning module receives the electrolysis activity coefficient transmitted by the sample quality monitoring module, determines whether the sample quality is normal, and displays an early warning for abnormal results; The warning result output module receives the abnormal result and transmits the result to the user terminal.

2. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: In the specimen knowledge base construction module, different specimen data are stored in the specimen knowledge base according to the specimen type, and the specimen data includes the temperature control range and the specimen storage conditions.

3. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: In the specimen receiving and checking module, the specimen receiving unit uses a barcode scanner to scan the unique identification code on the specimen, enters the identification information into the system, the system receives and calibrates the specimen, and outputs the corresponding specimen data in the specimen knowledge base. The specimen checking unit checks whether the specimen is complete and whether necrosis occurs, and confirms receipt of the specimen after verification.

4. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: In the process data acquisition module, the machine data acquisition unit uses sensors to monitor the operation data of the machine in the sample processing process in real time, including the pressure value in the atmospheric state, the pressure loss value before the stage during the operation of the machine, the pressure value in the exhaust state, the pressure loss value after the stage during the operation of the machine, and the air volume flow before the stage during the operation of the machine. The sample parameter acquisition unit uses sensors to obtain sample quality parameter data, including the charge of positive ions in the electrolysis process, the charge of negative ions in the electrolysis process, the current density in the electrolysis process, the viscosity of the sample electrolysis solvent, and the molar conductivity before dilution.

5. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: In the machine fault detection module, based on the operation data transmitted by the process data acquisition module, the specific content of the machine operation power coefficient is calculated by the operation monitoring mathematical model as follows: Step S01: using an automated specimen processing machine to process the received specimen; Step S02: Calculate the pre-stage pressure during the operation of the machine, and the calculation formula is: in Indicates the pressure before the stage during the operation of the machine. Indicates the pressure value under atmospheric conditions. Indicates the pre-stage pressure loss value during the operation of the machine; Step S03: Calculate the post-stage pressure during the operation of the machine, and the calculation formula is: in Indicates the post-stage pressure during the operation of the machine. Indicates the pressure value under exhaust state. Indicates the post-stage pressure loss value during the operation of the machine; Step S04: Calculate the machine operation compression power according to step S02 and step S03, and the calculation formula is: Where Wp represents the machine operation compression power, Indicates the volume flow of air before the stage during the operation of the machine. represents the adiabatic index; Step S05: Calculate the machine operation power coefficient, the calculation formula is: in Indicates the machine operating power coefficient, Indicates the adiabatic efficiency of the machine operation.

6. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: The machine fault warning module receives the machine operation power coefficient transmitted by the machine fault detection module , and the machine operating power coefficient With the preset machine operating power threshold Compare and judge whether the machine is running normally. If the machine power coefficient is Greater than the preset machine operating power threshold , the machine is judged to be operating abnormally, an abnormal result warning is displayed, and the warning information of the abnormal result is transmitted to the warning result output module. If the machine operating power coefficient Less than or equal to the preset machine operating power threshold , it is judged that the machine is operating normally.

7. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: In the sample quality monitoring module, based on the sample quality parameters transmitted by the process data acquisition module, the specific content of the electrolytic activity coefficient of the sample is calculated by the sample quality monitoring model as follows: Step S01: Calculate the dielectric constant of the specimen using the following formula: in represents the dielectric constant of the specimen, It represents the charge of positive ions during electrolysis. Indicates the charge of negative ions during electrolysis. is the current density during electrolysis, represents the dielectric constant of the specimen, T represents the thermodynamic temperature; Step S02: Calculate the viscosity coefficient of the specimen, the calculation formula is: in represents the viscosity coefficient of the specimen, Indicates the viscosity of the specimen electrolysis solvent; Step S03: Calculate the electrolytic dilution molar conductivity according to step S01 and step S02, and the calculation formula is: in represents the electrolytic dilution molar conductivity, represents the molar conductivity before dilution, Indicates the current value during the electrolysis process; Step S04: Calculate the electrolytic activity coefficient of the sample, the calculation formula is: in Represents the electrolytic activity coefficient of the specimen.

8. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: The sample quality warning module receives the electrolysis activity coefficient transmitted by the sample quality monitoring module. , and the electrolytic activity coefficient With the preset electrolytic activity threshold Compare and judge whether the sample quality is normal. If the electrolysis activity coefficient Less than the preset electrolytic activity threshold , the sample quality is judged to be abnormal, an abnormal result warning is displayed, and the warning information of the abnormal result is transmitted to the warning result output module. If the electrolysis activity coefficient Greater than or equal to the preset electrolytic activity threshold , it is judged that the machine is operating normally.

9. A laboratory specimen operation process monitoring system according to claim 1, characterized in that: The warning result output module receives the warning information transmitted by the machine failure warning module and the specimen quality warning module, and transmits the warning result to the user terminal through information reminder.

10. A laboratory specimen operation process monitoring method, used for using a laboratory specimen operation process monitoring system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step S1: storing different specimen data into a specimen knowledge base according to the specimen type, and generating a unique identification code for each specimen; Step S2: Use a barcode scanner to scan the unique identification code on the specimen, verify the specimen information, and confirm receipt of the specimen after verification; Step S3: using sensors to monitor the machine operation data and sample quality parameter data in real time during the sample processing process; Step S4: Based on the operation data transmitted by the process data acquisition module, the machine operation power coefficient is calculated through the operation monitoring mathematical model; Step S5: judging whether the machine is operating normally according to the machine operating power coefficient, and displaying an early warning of abnormal results; Step S6: Based on the sample quality parameters transmitted by the process data acquisition module, the electrolysis activity coefficient of the sample is calculated by the sample quality monitoring model; Step S7: judging whether the sample quality is normal based on the electrolytic activity coefficient, and displaying an early warning for abnormal results; Step S8: Receive abnormal results and transmit the results to the user terminal.

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